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20162022
most citedDeep Learning for Classification of Hyperspectral Data: A Comparative Review

687 citations · 778 across the 10 of their papers we have counts for

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cs.CV2022

Detection of Degraded Acacia tree species using deep neural networks on uav drone imagery

Anne Achieng Osio, Hoàng-Ân Lê, Samson Ayugi +3

Deep-learning-based image classification and object detection has been applied successfully to tree monitoring. However, studies of tree crowns and fallen trees, especially on floo…

cs.CV2022

CroCo: Cross-Modal Contrastive learning for localization of Earth Observation data

Wei-Hsin Tseng, Hoàng-Ân Lê, Alexandre Boulch +2

It is of interest to localize a ground-based LiDAR point cloud on remote sensing imagery. In this work, we tackle a subtask of this problem, i.e. to map a digital elevation model (…

cs.CV2020

Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study

Javiera Castillo-Navarro, Bertrand Le Saux, Alexandre Boulch +2

The development of semi-supervised learning techniques is essential to enhance the generalization capacities of machine learning algorithms. Indeed, raw image data are abundant whi…

cs.CV20201 cited

Localize to Classify and Classify to Localize: Mutual Guidance in Object Detection

Heng Zhang, Elisa Fromont, Sébastien Lefevre +1

Most deep learning object detectors are based on the anchor mechanism and resort to the Intersection over Union (IoU) between predefined anchor boxes and ground truth boxes to eval…

cs.CV20201 cited

Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks

Heng Zhang, Elisa Fromont, Sébastien Lefevre +1

Multispectral images (e.g. visible and infrared) may be particularly useful when detecting objects with the same model in different environments (e.g. day/night outdoor scenes). To…

cs.CV2020

GeoGraph: Learning graph-based multi-view object detection with geometric cues end-to-end

Ahmed Samy Nassar, Stefano D'Aronco, Sébastien Lefèvre +1

In this paper we propose an end-to-end learnable approach that detects static urban objects from multiple views, re-identifies instances, and finally assigns a geographic position…